To bridge this gap, this paper establishes a transfer learning protocol for evaluating ViT models on object detection and instance segmentation using the COCO dataset [1]} and the Mask R-CNN framework. We focus on standard ViT models, with minimal modifications, as defined in the original ViT paper [2]}, because we expect this architecture will remain popular in unsupervised learning work over the next few years due to its simplicity and flexibility when exploring new techniques, , masking-based methods [3]}, [4]}.
